Development Of Golf Putting Learning Model Based On Synthetic Grass

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Vol 6, No 1.2, 2022

Development Of Golf Putting Learning Model Based On Synthetic Grass

Aep Rohendi

Program Studi PJKR, STKIP Pasundan [email protected]

* corresponding author

I. Introduction

Golf is a lifelong sport activity that can be taught in physical education classes [18]. The game of golf is played on a large field starting with playing the ball from the teeing area where players must overcome the challenges of the penalty area, bunker area and green area or putting green before being able to enter the ball into the hole. [17]. Putting green is the most important part of the entire golf course which can be made into a mini putting green both indoors and outdoors using Envirofill synthetic grass which has been designed to help ball speed and ball roll consistency.[2]. Putting green is a special place that is prepared to play the ball around the flagpole by placing the ball or putting it into the hole with a stick called a putter according to the rules that have been set. [15]. Putting requires the integration of sensory input and motor coordination to optimize the tempo, rhythm and balance of style patterns in each swing section, namely back swing, down swing, impact, follow through, and finish [11]. According to Newton's second law, putting requires that speed and acceleration are inversely proportional to mass, meaning that the greater the object's load, the smaller the object's speed and acceleration, and vice versa (Ratni Sirait, 2018). So in essence putting requires an understanding of the causal relationship between action and outcome [9]. Thus putting learning needs to be improved continuouslys.

The results of previous research that interactive metronome (IM) training in female golfers can improve consistency and can improve brain connectivity from the cerebellum to the frontal cortex which plays an important role in motor control during putting (Kim et al., 2018). Mental imagery is a series of activities to imagine, or bring back in the mind a motor experience that has been stored in memory, according to what has been seen and experienced in motor learning so that it becomes a mental training tool that can be used effectively [3]. The Educational Game Tool (EGT) can be used as a medium for learning the game of golf [5]. The play model can help encourage changes in student skills, especially in putting learning in creating an effective learning atmosphere [14]. Putting technique is one of the most difficult golf techniques about 40% of the number of strokes made by players on the course [7]. The nipple measuring instrument can use a large circle with a diameter of 14 cm and a small circle of 7 cm with 10 putting times for pre-test and post-test [6].

Based on the results of preliminary studies, the results of previous research, as well as the experience of researchers on golf courses for decades that the game of golf in the green area is very

ARTICLE INFO A B S T R A C T

Article history:

Received 17 June 2022 Revised 04 Sept 2022 Accepted 01 Nov 2022

The game of golf takes place in various conditions of land contours that vary greatly and must be adapted to different distances, these are all challenges that must be faced by golfers to understand the variables that underlie performance.

The main objective of this research is to improve putting skills effectively through the development of a synthetic grass-based golf putting learning model at STKIP Pasundan. The method used to uncover this problem are the Research

& development (R & D) development method from Borg and Gall. The sample of this study amounted to 40 students, 20 people as the control group and 20 people as the experimental group. Based on the gain index calculation, it was found that the gain for the experimental class was 0.705. This value is interpreted as being in the high category. Soit can be concluded that the development of an effective synthetic grass-based putting golf learning model.

Keywords:

Development, model,

Golf Putting Learning, Synthetic Grass

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difficult to play putting and inserting the ball into the hole or holes effectively. So for this reason, the researchers tried to develop a synthetic grass-based putting golf learning model. The aim of this research is to make it easier for golfers to get a good putt every time they hit a putter when they put the ball into the hole. The golf putting learning model developed is the AFR model, namely: (1) match play game in the form of a game where a player or party is dealing directly with an opponent or opposing party, a player who wins a hole is the number of strokes less. (2) Stroke Play is a form of play in which a player or party competes against all players. The winner is the player or side that completes all rounds with stableford, maximum score par/bogey. This game model is simulated on synthetic grass. The learning media used were: synthetic grass golf carpet, thin hulahoop made of plastic with a diameter of 75 cm, a flagpole of 50 cm (Pin), a golf putter, a golf ball and a camera.

While the theory supporting the basics of the synthetic grass-based golf putting learning model technique refers to the concept of the preliminary study results, namely: (1) Preparation: Both feet are shoulder-width apart, the body is slightly leaning forward, both hands holding the sticks together with the grip overlapping technique, the shoulders and arms form a triangle, left eye focused on the ball.

(2) Execution of motion: Shoulders, arms, and sticks become a single unit ready to be swung backwards (back swing), when performing a swing (swing) forward only the shoulders and arms are moved to push the ball towards a circle with a diameter of 75 cm naturally, the position of the ball is behind the front foot, the target swing at a predetermined point. (3) Advanced movements: in the implementation of the back swing, down swing, impact, follow through, finish as well as feeling, rhythm, rhythm and body balance into one unit when swinging.

Figure 1 Development of a Golf Putting Learning Model Based on Synthetic Grass

The development of synthetic grass-based putting golf learning model consists of 40 students divided into 8 groups consisting of 5 students or according to the needs of the model and the number of students. The image model above can be further developed into a variety of more playing models.

The formula for setting the gauge in putting, is R (Radius) = 9ft / 3m (Distance from the edge of the hole or flagpole) R = 9ft /3m (Lawrie Montague and David Milne, 2014)

II. Methods

In this development research using the Research & development (R & D) development model from Borg and Gall which is a process for developing and validating learning products. Adapted to the needs of research that begins with (1) a preliminary study conducting research and gathering information. (2) planning (3) developing the initial product form. (4) conduct a preliminary field test.

(5) revise the main product. (6) Conducting main field trials. (7) Expert Revision. (8) Completion of the results of field trials. (9) final product revision. (10) Final Product Dissemination and Implementation (Borg & Gall, 1983).

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III. Result and Discussion

The results of the effectiveness test of the development of synthetic grass-based putting golf learning models were obtained from the results of trials in the control group and the experimental group. Overall, the description of the control class and the experimental class at the pretest and posttest stages can be presented in the following table (Hernawan et al., 2018).

Table 1 Description of Control and Experiment Class

Test Class N Average Standard Deviation

Pre-Test Control 40 6,025 1,097

Experiment 40 5,900 0,955

Final Test Control 40 6,825 0,712 Experiment 40 10,200 0,939 Source: Data Processing, 2022

The table above shows that in the initial test, the control class average (6.025) was above the experimental class average (5.900). As for the final test, the average control class (6.825) is far below the experimental class (10.200). The following is an illustration of the description of the control and experimental classes for the initial and final tests.

Referring to the results of the previous description, the description of the control class at the pretest and posttest stages can be presented in the following table.

Table 2 Description of Control Class

Class Class N Average Standar Deviation Control Pre-Test 40 6,025 1,097 Final Test 40 6,825 0,712 Source: Data Processing, 2022

The average increase (or decrease) from the initial test to the final test for this control class is 0.8, as shown in the following figure.

Referring to the results of the previous description, the description of the experimental class at the pretest and posttest stages can be presented in the following table.

Table 3 Experiment Class Description

Class Class N Average Standard Deviation Experiment Pre-Test 40 5,900 0,955

Post-Test 40 10,200 0,939 Source: Data Processing, 2022

The average increase (or decrease) from the initial test to the final test for this experimental class is 4.3, as shown in the following figure.

Before testing the two-average difference test hypothesis, the data requirements test was first carried out. In this case, several statistical assumptions need to be met, namely normality and homogeneity. In the following, each calculation of the statistical assumptions is presented.

The normality test in this case is calculated using the Kolmogorov-Smirnov test. The results of calculating the normality of the data for each class and test are presented as follows.

Table 4 Normality test

PRE_KON PRE_EKS POS_KON POS_EKS

N 40 40 40 40

Normal Parameters Mean 6.025 5.900 6.825 10.200

Std. Deviation 1.097 0.955 0.712 0.939

Most Extreme Differences Absolute 0.184 0.208 0.247 0.228

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Positive 0.184 0.208 0.228 0.159

Negative -0.166 -0.192 -0.247 -0.228

Kolmogorov-Smirnov Z 1.164 1.318 1.563 1.441

Asymp. Sig. (2-tailed) 0.133 0.062 0.126 0.131

Critical value 0.050 0.050 0.050 0.050

Decision Normal Normal Normal Normal

The significance value for each class and test is greater than 0.05. Thus it can be concluded that all classes and tests are normally distributed. Homogeneity test is a test of whether or not the variances of two or more distributions are equal. The homogeneity test here uses the Variance Homogeneity Test. Homogeneity test was conducted to determine whether the data in the two samples were homogeneous or not. This homogeneity test is to compare the variance or standard deviation for each paired data. Here are the results of the homogeneity test for each class.

Table 5 Homogeneity Test

Class Tes Standard

Deviation

F-Value F-table Decission

Control Pre-Test 1,097 1,541 4,091 Homogen

Post-Test 0,712

Experiment Pre-Test 0,955 1,017 4,091 Homogen

Post-Test 0,939

Source: Data Processing, 2022

From this calculation, the F-count value for Class Control is 1.541 and F-table = 4.091. It appears that F count < F table. This means that the Pre-Test and Post-Test data for Class Control are homogeneous. This calculation also applies to Class Experiments where the F-count for Class Experiment is 1.017 and F-table = 4.091 so it can be stated that the Pre-Test and Post-Test Class Experiment data are homogeneous.

Overall, testing the requirements of the hypothesis shows that all classes and tests are normally distributed. In addition, the calculation results show that the class and the test are homogeneous. Thus the two-average test using the t-test (parametric) can be used.

Testing this hypothesis using a two-average t-test. The statistical hypothesis is:

H0: k – e = 0; there is no average difference between Class Control and Class Experiment after the implementation of the putting learning model with a play approach.

H1: k – e ≠ 0; there is no average difference between Class Control and Class Experiment after the implementation of the putting learning model with a play approach.

The results of the calculation of the difference between the two averages using the t-test resulted in the following calculations:

Table 6 Hypothesis testing N Levene's Test for

Equality of Variances

t-test for Equality of Means

F Sig. T Df Sig. (2-

tailed)

Mean Difference

Keterangan Pre-

Test

40 0,502 0,481 0,543 78 0,588 0,125 There is no difference Post-

Test

40 3,877 0,052 -

18,111

78 0,000 -3,375 There is a difference

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The calculation shows that in the Pre-Test there is no average difference between Class Control and Class Experiment. Thus, it was concluded that at first, there was no difference in the students' ability in putting skills in Class Control and Experiment. In other words the abilities of Class Control and Experiment were originally the same.

In the Post-Test, it was found that there was a significant difference between Class Control and Class Experiment. This can be seen from the t-count value of 18.111 which is greater than the t-table value, which is 2.022. In other words, the ability of the Class Experiment to get a putting learning model with a play approach is significantly different from the ability of Class Control.

To determine the increase in the pretest and posttest scores of Class Experiment and Class Control, the gain index calculation is used. In this study, the gain index is used if the average value of the Class Experiment posttest and the Class Control posttest is different. Gain index formula (g) (Meltzer, 2002). is as follows:

𝑔 =𝑠𝑐𝑜𝑟𝑒_𝑝𝑜𝑠𝑡 − 𝑡𝑒𝑠𝑡 − 𝑝𝑟𝑒𝑡𝑒𝑠𝑡 𝑠𝑐𝑜𝑟𝑒 𝑆𝑐𝑜𝑟𝑒_𝑚𝑎𝑥𝑖𝑑𝑒𝑎𝑙 − 𝑠𝑐𝑜𝑟𝑒_𝑝𝑟𝑒𝑡𝑒𝑠

Table 7 Normalized Gain Value Interpretation Classification Normalized Gain Value Interpretation

g > 0,70 Tall

0,30 < g ≤ 0,70 Currently

g ≤ 0,30 Low

The gain index for Class Experiment is:

𝑔 =10,2 − 5,9 12 − 5,9 𝑔 = 0,705

Based on the gain index calculation above, it was found that the gain for Class Experiment was 0.705. This value is interpreted as being in the high category. In other words, students' abilities can increase after being given the development of a more detailed synthetic grass-based golf putting learning model, while the playing approach is more similar to the actual putting game, namely:

learning starts from easy to moderate level and gets more complex.

Discussion

A. Preliminary Study

The learning model developed consists of a hierarchical arrangement from learning (warm-up and light exercise) to putting learning (individual and group). Preparation: Both feet are shoulder-width apart, body slightly leaning forward, both hands holding the sticks together with the grip overlapping technique, shoulders and arms form a triangle, left eye focused on the ball. (2) Execution of motion:

Shoulders, arms, and sticks become a single unit ready to be swung backwards (back swing), when performing a swing (swing) forward only the shoulders and arms are moved to push the ball towards a circle with a diameter of 75 cm naturally, the position of the ball is behind the front foot, the target swing at a predetermined point. (3) Advanced movements: in the implementation of the back swing, down swing, impact, follow through, finish as well as feeling, rhythm, rhythm and body balance into one unit when swinging.

B. Planning

Based on the input data above. At this stage a draft or design of the putting learning model is drawn up based on the results of the needs analysis, namely: the putting learning model is very urgent to be developed in connection with the lack of training models written specifically for the benefit of the putting learning model. Putting learning model products are very possible to be developed. After

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the draft is made, the validation of golf training and learning experts is carried out whose data acquisition is as follows:

Table 8 Golf Training Expert Validation Results Data N = 3 with an instrument of 50 question items No Draft training model

according to… Minimum Score

Maximum Score

Result Score

Percentage

1 Golf Training Expert 1 50 200 178 89,00

2 Learning Expert 2 50 200 172 86,00

3 Learning Expert 3 50 200 198 99,00

Average 91,33

C. Product development

Development of synthetic grass-based putting golf learning model. The aim of this model is to improve the golf putting skills of students and golfers to be more effective in getting putt results.

The golf putting learning model developed is the AFR model, namely: (1) match play game in the form of a game where a player or party is dealing directly with an opponent or opposing party, a player who wins a hole is the number of strokes less. The game is only played in the green area.

(2) Stroke Play is a form of play in which a player or party competes against all players. The winner is the player or side that completes all rounds with stableford, maximum score par/bogey.

The game is only played in the green area.

D. 1st field trial

Small group field trials are carried out repeatedly so that a more ideal model developer draft is obtained so that the results of this small group trial can be used for greater learning.

E. Product revision

The three golf training experts gave input that this kind of exercise should be added to the number of groups or groups more so that all students have more opportunities to try.

F. Field trial 2

In the second trial the problems that occurred began to be solved. After the problems are collected, there are improvements in implementation. Improvements made after the second meeting in the small group test can be applied to the large group test.

G. Expert revision

After revising the first stage of the product, then a small group trial was carried out which resulted in conclusions and input to be revised in the next treatment. The revisions carried out were: Increasing the number of drill portions or individual repetitions of exercises, developing other possibilities or alternatives from existing alternatives. While the field notes that have been obtained are: Fun games or team exercises are important to see how much progress has been achieved after physical exercises or individual exercises have been carried out.

H. Product improvement

Based on the analysis of the validation data obtained, it can be seen the feasibility of the golf putting learning model developed. Validation of golf training experts, with the test results as the basis for making conclusions whether the golf putting learning model is feasible or not to be used. In table 8 above, it can be read that the average percentage of data analysis from three golf training items is 91.33%. It can be concluded that the putting golf game learning model with a synthetic grass-based playing approach is very feasible to use.

I. Final revision

According to experts, putting learning with a playing approach no longer requires revision.

However, from the field trials, field notes are still included. Field notes obtained from field trials are that the learning system is good, just consider the duration of time in each putting learning session with a play approach, with the size must be in accordance with the lesson plan.

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Vol 6, No 1.2, 2022

J. Product dissemination

Make reports on products in journals, work with publishers who can do commercial distribution..

IV. Conclusion

The aim of this research is to make it easier for golfers to get a good putt every time they hit a putter when they put the ball into the hole. This innovative development of a putting learning model with a synthetic grass-based play approach has made a positive contribution to the achievement of effective putting learning. The developed model is (1) match play game in the form of a game where a player or party is dealing directly with an opponent or opposing party, a player who wins a hole is the number of strokes less. (2) Stroke Play is a form of play in which a player or party competes against all players. The winner is the player or side that completes all rounds with stableford, maximum score par/bogey. Problems in putting play account for about 40% of the number of strokes a player makes on the court. To solve this problem requires a learning development model with the integration of sensory input and motor coordination to optimize the feeling of rhythm, rhythm and body balance when swinging. In essence, the swing motion sequence consists of back swing, down swing, impact, follow through, and finish. Calm play can support Control (emotions) to stay stable and succeed in getting par [4].

References

[1] Borg & Gall. (1983). Education (4, Research Th Longman, ed.). New York: Inc.

[2] Brad Borgman. (2020). The best artificial grass putting green for leisure golfers.

https://blog.usgreentech.com/landscape/the-best-artificial-grass-for-golf-putting-greens [3] Brouziyne & Molinaro. (2015). Mental Imagery Combined with Physical Practice of

Approach Strokes for Golf Beginners. Sage, Volume: 10, p.1. https://doi.org/10.2466

[4] David Milne. (2017). David Milne Pelatih Nasional Golf Indonesia Di Ajang Turnamen golf South East Asian Amateur Golf Team Championship 2017 Title.

[5] Dea, Yusup, Anwar, Ayu, C. (2021). Alat Permainan Edukatif Golf Anak Usia Dini sebagai Program Edupreneur Prodi Pendidikan Anak. Tumbuh Kembang, 6, 1.

https://doi.org/https://doi.org/10.14421/jga.2021.61-03

[6] Gal Ziva, Matar Ochayona, R. (2018). Enhanced or diminished expectancies in golf putting – Which actually affects performance? Psychology of Sport and Exercise, Volume 40, Pages 82-86. https://doi.org/10.1016/j.psychsport.2018.10.003

[7] Gogo. (2022). Mau Menang Main Golf. https://gogolf.co.id/golf-course/teknik-putter-golf [8] Hernawan, Widiastuti, Apriliaintan, & Pradityana. (2018). Pengembangan Model Pengenalan

Air Anak Usia Din. JURNAL PENDIDIKAN USIA DINI, Volume 12.

http://journal.unj.ac.id/unj/index.php/jpud

[9] Hideyuki TanakaMasato Iwami. (2018). Estimating Putting Outcomes in Golf: Experts Have a Better Sense of Distance. Perceptual and Motor Skills. https://doi.org/DOI:

10.1177/0031512518754467

[10] Kim, J. H., & Han, J. K. (2018). Training eﬀects of Interactive Metronome® on golf performance and brain activity in professional woman golf players. Human Movement Science, p.p.1,2. https://doi.org/10.1016

[11] Kim, J. H., Han, J. K., & Han, D. H. (2018). Training eﬀects of Interactive Metronome® on golf performance and brain activity in professional woman golf players. Human Movement Science, 1. https://doi.org/doi.org/10.1016

[12] Lawrie Montague and David Milne. (2014). The Elite Golfer Improvement System. In Hard Core. Published by Pro Tour Golf College Country Club Boulevard, Connolly, Western Australia 6027. www.ProTourGolfCollege.com

[13] Meltzer, D. E. . (2002). The Relationship Between Mathematics PreparationAnd conceptual learning gain in physics:A possible inhidden Variablei in Diagnostic pretest scores. Volume 70. https://doi.org/DOI: 10.1119/1.1514215

[14] Muhammad Reza Atsani. (2020). Meningkatkan kemampuan passing bawah bolavoli

menggunakan metode bermain. Physical Education, 89.

https://doi.org/https://doi.org/10.25299/es:ijope.2020.vol1(2).5592

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[15] R&A USGA. (2019). Player’s Edition The Rules Golf. https://widget.randa.org/id- id/rog/2019/rules/players-edition/rule-13

[16] Ratni Sirait. (2018). Pengaruh Massa Terhadap Kecepatan dan Percepatan Berdasarkan Hukum II Newton Menggunakan Linier Air Track. Ilmu Fisika Dan Teknologi, 2, No. 2 1, 1.

https://doi.org/2580-6661

[17] Rohendi & Suwandar. (2017). Belajar dan Berlatih Golf Usia Dini (1st ed.). 2017.

[18] Shot Scarboro & Tony Pritchard. (2019). Using Sport Education to Teach the Lifetime Sport of Golf. The Physical Educator. https://doi.org/DOI: 10.1080/07303084.2015.1085342